code: def simulated_timeseries(
      n_subjects,
      n_TRs,
      n_voxels=30,
      noise=1,
      data_type="array",
      random_state=None,
  ):
      prng = np.random.RandomState(random_state)
      if n_voxels:
          signal = prng.randn(n_TRs, n_voxels)
          prng = np.random.RandomState(prng.randint(0, 2**32 - 1))
          data = [
              signal + prng.randn(n_TRs, n_voxels) * noise
              for subject in np.arange(n_subjects)
          ]
      elif not n_voxels:
          signal = prng.randn(n_TRs)
          prng = np.random.RandomState(prng.randint(0, 2**32 - 1))
          data = [
              signal + prng.randn(n_TRs) * noise
              for subject in np.arange(n_subjects)
          ]
      if data_type == "array":
          if n_voxels:
              data = np.dstack(data)
          elif not n_voxels:
              data = np.column_stack(data)
      return data
  
file_path: brainiak/tests/isc/test_isc.py
imports: import numpy
  import logging
  import pytest
  from brainiak.isc import isc, isfc, bootstrap_isc, permutation_isc, squareform_isfc, timeshift_isc, phaseshift_isc
  from scipy.spatial.distance import squareform
label_desc: Use image node_id 1 for calling a global function with example usage: simulated_timeseries(n_subjects, n_TRs, n_voxels, noise, data_type, random_state) and returns: data